DP-600 · Question #40
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might h
The correct answer is B. No. The df.explain() PySpark expression does not calculate descriptive statistics like min, max, mean, and standard deviation.
Question
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have a Fabric tenant that contains a new semantic model in OneLake. You use a Fabric notebook to read the data into a Spark DataFrame. You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns. Solution: You use the following PySpark expression: df.explain() Does this meet the goal?
Options
- AYes
- BNo
How the community answered
(35 responses)- A14% (5)
- B86% (30)
Why each option
The `df.explain()` PySpark expression does not calculate descriptive statistics like min, max, mean, and standard deviation.
`df.explain()` provides query plan details, not statistical summaries of data content.
The `df.explain()` method is used to print the logical and physical execution plan of a DataFrame query, helping to understand how Spark will process the data, not to compute statistical aggregates.
Concept tested: PySpark DataFrame query plan inspection
Source: https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.explain.html
Topics
Community Discussion
No community discussion yet for this question.